Results 251 to 260 of about 27,406 (291)
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Error Entropy and Mean Square Error Minimization for Lossless Image Compression
2006 International Conference on Image Processing, 2006In this paper, the minimum error entropy (MEE) criterion is considered as an alternative to the mean square error (MSE) criterion in obtaining predictor coefficients for lossless still image coding. Estimation of the error entropy is done using Renyi's formula.
Michael W Hoffman
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Mean square error minimization in a nonstationary system
IEEE Transactions on Automatic Control, 1965This paper gives a method for the analysis and synthesis of a controller to track a time-varying optimum operating point. In particular, the maximization of the average of a quadratic performance index of a general system is considered where not only the location of the maximum is time-varying, but also the shape of the performance index function.
A. Lavi, E. Mastascusa
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2008 10th Brazilian Symposium on Neural Networks, 2008
In this paper, artificial neural networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and Technological Biology - IBET in Oeiras - Lisbon - Portugal.
Adriana Rosa Garcez Castro +2 more
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In this paper, artificial neural networks (ANN) are used to model an extraction process that uses a supercritical fluid as solvent which its pilot installation is located at the Institute of Experimental and Technological Biology - IBET in Oeiras - Lisbon - Portugal.
Adriana Rosa Garcez Castro +2 more
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Mean-Square Error Minimization of Boiling Reactor Noise [PDF]
Nuclear Science and Engineering, 1962 An analytical approach is taken to develop a model of an optimum linear control system for a linearized approximation to a boiling water reactor. The optimization criterion used is the minimization of the mean-square error of the random fluctuation in the output variable resulting from boiling voids.
Lynn E. Weaver, Kenneth R. Katsma
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Minimization of mean-square error for data transmitted via group codes
IEEE Transactions on Information Theory, 1969We show how to find solutions to the problem considered by Mitryayev [l ] in the case where the loss power function is quadratic. This problem is to minimize mean-square error when digital data is represented by group code combinations and the a priori probability distribution is uniform.
Thomas R. Crimmins +3 more
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Designs in nonlinear regression by stochastic minimization of functionals of the mean square error matrix [PDF]
Journal of Statistical Planning and Inference, 2006 We reconsider and extend the method of designing nonlinear experiments presented in Pázman and Pronzato (J. Statist. Plann. Inference 33 (1992) 385). The approach is based on the probability density of the LS estimators, and takes into account the boundary of the parameter space. The idea is to express the elements of the mean square error matrix (MSE)
Gauchi, Jean-Pierre, Pázman, A.
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IEEE Transactions on Signal Processing, 2011
In the past, ad hoc methods have been used to choose gains in proportionate-type normalized least mean-square algorithms without strong theoretical under-pinnings. In this correspondence, a theoretical framework and motivation for adaptively choosing gains is presented, such that the mean-square error will be minimized at any given time. As a result of
Kevin Wagner, M Doroslovacki
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In the past, ad hoc methods have been used to choose gains in proportionate-type normalized least mean-square algorithms without strong theoretical under-pinnings. In this correspondence, a theoretical framework and motivation for adaptively choosing gains is presented, such that the mean-square error will be minimized at any given time. As a result of
Kevin Wagner, M Doroslovacki
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A binning formula of bi-histogram for joint entropy estimation using mean square error minimization
Pattern Recognition Letters, 2018Abstract Histograms have extensively been used as a simple tool for nonparametric probability density function estimation. However, practically, the accuracy of some histogram-based derived quantities, such as the marginal entropy (ME), the joint entropy (JE), or the mutual information (MI) depends on the number of bins chosen for the histogram.
Abdenour Hacine-Gharbi +1 more
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Structural and Multidisciplinary Optimization, 2020
Surrogate models are often used as surrogates for computationally intensive simulations. And there are a variety of surrogate models which are widely used in aerospace engineering–related investigation and design. In general, there is an optimal individual surrogate for a certain research object.
Yifan Ye, Xiaobo Zhang, Ye Yifan
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Surrogate models are often used as surrogates for computationally intensive simulations. And there are a variety of surrogate models which are widely used in aerospace engineering–related investigation and design. In general, there is an optimal individual surrogate for a certain research object.
Yifan Ye, Xiaobo Zhang, Ye Yifan
exaly +2 more sources
Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error
IEEE Transactions on Neural Networks and Learning Systems, 2017The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers.
Xinjiang Lu, Minghui Huang
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